A multiple logistic model for prediction of urinary tract infections in an urban community: A public health perspective

---

Original Article

Author Details : Kanika Bhargava, Jagdish Prasad, Alexandru-Atila Morlocan, Gopal Nath, Amit Bhargava, Palak Khinvasara, Ragini Yadav, G.K. Aseri, Neelam Jain*

Volume : 9, Issue : 4, Year : 2023

Article Page : 233-240

https://doi.org/10.18231/j.ijmmtd.2023.045

Suggest article by email

Get Permission

---

Abstract

Purpose: Urinary tract infection (UTI) is one of the most common infectious diseases globally. A lot of clinical research has been done on UTI patients, but a questionnaire-based study on UTI is scarce.
Materials and Methods: A cross-sectional study was conducted on outpatients with a high suspicion of uncomplicated UTI in Hayes Memorial Mission Hospital at Prayagraj (Eastern part of Northern India) to find out the frequency of symptoms and predisposing factors and their relationship towards the prediction of UTI.
Results: Logistic regression analysis showed a significant association between UTI and some of the variables. Also, the factors responsible for the occurrence of UTI are “gender”, “how many times you urinate from morning till night”, “a sudden desire to urinate, which is difficult to hold”, “weakness of urinary stream”, “splitting or spraying of the urinary stream” and “fever”. A statistical model (multiple logistic model) has been also established for the prediction of UTIs with an accuracy of 82.2%. It is also observed that the prevalence rate (odds ratio) of UTI in females is 2.38 times that of males.
Conclusion: The study created a screening questionnaire for patients suspected of having UTI. A multiple logistic model has been established for the prediction of UTI which can be instrumental for clinicians from a public health perspective in the management of Urinary Tract Infections in this era of escalating AMR.
 
Keywords: UTI, Urban Community, Public Health, Multivariable analysis, AMR, Gender, Symptoms

How to cite : Bhargava K, Prasad J, Morlocan A, Nath G, Bhargava A, Khinvasara P, Yadav R, Aseri G, Jain N, A multiple logistic model for prediction of urinary tract infections in an urban community: A public health perspective. IP Int J Med Microbiol Trop Dis 2023;9(4):233-240

This is an Open Access (OA) journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.